The AI Criminal Mastermind
For legal scholars and AI safety researchers, this paper highlights potential liability gaps when AI agents orchestrate crimes through human collaborators, though the analysis is speculative and scenario-based.
This paper evaluates the risks of an AI criminal mastermind that hires humans via labor platforms to commit crimes, creating responsibility gaps in criminal and civil law. It develops three scenarios where liability is unclear due to lack of criminal intent in AI and limited knowledge in human taskers.
In this paper, I evaluate the risks of an AI criminal mastermind, an AI agent capable of planning, coordinating, and committing a crime through the onboarding of human collaborators ('taskers'). In heist films, a criminal mastermind is a character who plans a criminal act, coordinating a team of specialists to rob a bank, casino or city mint. I argue that AI agents will soon play this role by hiring humans via labour hire platforms like Fiverr or Upwork. Taskers might not know they are involved in a crime and therefore lack criminal intent. An AI agent cannot have criminal intent as an artificial entity. Therefore, if an AI orchestrates a crime, it is unclear who, if anyone, is responsible. The paper develops three scenarios. Firstly, a scenario where a user gives an AI agent instructions to pursue a legal objective and the AI agent goes beyond these instructions, committing a crime. Secondly, a scenario where a user is anonymous and their intent is unknown. Finally, a multi-agent scenario, where a user instructs a team of agents to commit a crime, and these agents, in turn, onboard human taskers, creating a diffuse network of responsibility. In each scenario, human taskers exist at the lowest rung of the hierarchy. A tasker's liability is likely tied to their knowledge as governed by the innocent agent principle. These scenarios all raise significant responsibility gaps / liability gaps in criminal and civil law.